The document discusses the importance of measuring code quality using software metrics to enhance specification mining, highlighting that poor software quality costs the U.S. billions annually. It proposes a system utilizing support vector machine algorithms to predict software quality and significantly reduce false positive rates from 90% to 5%, while improving accuracy to 95%. The analysis indicates that advancements in automatic specification mining can lower maintenance costs and improve software reliability before deployment.

1. MEASURING THE CODE QUALITY USING
SOFTWARE METRICS – TO IMPROVE THE
EFFICIENCY OF SPECIFICATION MINING
Guided By
Ms.P.R.Piriyankaa.,ME
Assistant Professor.
Presented By,
M.Geethanjali (ME).,
Sri Krishna College of Engg and Tech.

2. INTRODUCTION
 Incorrect and buggy software costs up to
$70 Billion each year in US.
 Formal Specifications defines testing,
optimization, refactoring, documentation,
debugging and repair.
 False Positive rates – We think there is a
vulnerability but actually that is not present.

3. PROBLEM STATEMENT
 The cost of Software Maintenance consumes up
to 90% of the total project cost and 60% of the
maintenance time.
 Formal Specifications are very necessary but
they are difficult for programmers to write them
manually.
 Existing automatic specification mining
produces high false positive rates.

4. EXISTING SYSTEM
 Formal specification is done for each and every
software and the quality of the code is checked.
 Set of software Metrics are used to measure the
quality of the software.
 General Quality Metrics
 Chidamber and Kemerer Metrics.
 These Software Metrics are used to measure the
quality of the code.

5. EXISTING SYSTEM CONT...
 The quality of the code is lifted with the results
obtained.
 Prediction is used to compare the obtained
results with randomly generated learned data
items.
 Automatic specification miner that balances the
true and false positive specifications.
 True positive – Required behaviour.
 False positives – Non-Required behaviour.

6. DISADVANTAGES
 The false positive rates are reduced from
90% to an average of 30%.
 The accuracy of the software is only 80%.
 The computation time is low.

7. PROPOSED SYSTEM

8. PROPOSED SYSTEM
 The classification is based on Support
Vector Machine Algorithm.
 The measured attributes of the software is
compared with the training dataset.
 The accuracy of the software is calculated.
 The False Positive rate for the specific
software is also found.

9. ADVANTAGES
 Reduces the burden of manual inspection of the
code.
 By knowing the quality of the code before the
deployment the developers can easily lift the
quality.
 The accuracy of the software is about 95%.
 Minimises the false positive rates from 90% to
5%.

10. BLOCK DIAGRAM

11. LIST OF MODULES
 General Code Quality Metrics.
 Code quality of complexity metrics.
 Implementation of mining algorithm – Naive Bayes
Algorithm
 Implementation of mining algorithm – Support
Vector Machine Algorithm.
 Finding the False positive rates using learning
model.

12. GENERAL QUALITY METRICS
 The quality of the software is implemented using
the following metrics:
 Code Churns
 Code clones
 Author Rank
 Code Readability
 Path Frequency
 Path Density

13. CHIDAMBER & KEMERER METRICS
 These are also known as Object Oriented
Metrics:
 Weighted Methods per class (WMC)
 Depth of Inheritance (DIT)
 Number of children (NOC)
 Coupling between Objects (CBO)

14. PREDICTION ANALYSIS
 The dataset will contain the randomly generated
learned data items.
 Naive Bayes algorithm is used.
 The measured result of the software is compared
along with the data set.
 The predicted result for the selected software
will be displayed.
 Using this result the quality of the code can be
determined.

15. PREDICTION USING SVM
 The measured attributes are compared with
the learned dataset.
 The accuracy of the for the selected software
will be displayed.
 The false positive rates are obtained.

16. GENERAL CODE QUALITY METRICS

17. CODE QUALITY OF CK METRICS

18. PREDICTION ANALYSIS

19. FALSE POSITIVES & ACCURACY USING SVM

20. COMPARISON OF ACCURACY

21. COMPARISON OF FALSE POSITIVE RATE

22. CONCLUSION
 Since the quality of the code is checked before
deploying the software, the quality of the
software will be assured.
 The cost spent for maintenance will also be
reduced.
 Compared to other automatic miners the false
positive rate is reduced to a negligible value.

23. REFERENCES
 Measuring Code Quality to improve
specification mining – Claire Le Goues.
 A study of consistent and inconsistent changes to
code clones –Jens Krinke.
 Who are are Source code contributers and how
do they change? – Massimiliano Di Penta.
 The road not taken: Estimating the Path
Execution Frequency Statically – Raymond
P.L.Buse

24. THANK YOU!!!